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CI: Pain catastrophizing total score (95% CI 1.027;
1.117), but it did confirm the results of the prediction
model.
To determine the performance of the prediction
model, a parameter of calibration and a parameter of
discrimination were calculated (30). For calibration,
the Hosmer-Lemeshow test identified a good fit for
the model (p = 0.508) (Table III). For discrimination
the ROC curve was calculated and its area under the
curve (AUC). The AUC for the model was 0.688 (95%
CI 0.589; 0.786) (Table III).
DISCUSSION
The aims of this prospective cohort study were to ex-
plore predictors for dropout of patients with chronic
musculoskeletal pain during an interdisciplinary pain
management programme, and to develop a multivariate
model to predict dropout. Based on the conceptual
framework of the E-CSM of Self-Regulation 18 po-
tential predictors were investigated for associations
with dropout. The results from univariate logistic
regression analysis identified 7 potential predictors
for dropout eligible for inclusion in multiple logistic
regression analyses. Just one of the potential predictors
was retained in the multiple logistic regression model;
the pain catastrophizing total score.
Relating findings to the literature
Since multivariate prediction models in different stu-
dies often contain different predictors and are therefore
not comparable, the findings from univariate analyses
in our study were compared with results from other
studies on dropout in IPMPs.
Although we focused in this prospective cohort
study on potential predictors that were derived from
the E-CSM of Self-Regulation, it is also important
to reflect on differences on other sociodemographic
baseline items between the dropouts (DG) and the
program-completers (CG). This study found significant
differences between the DG and the CG in educational
level: there were more patients with low educational
levels in the DG vs. the CG. Despite the fact that the
findings of our systematic review revealed no signi-
ficant results for educational level as a predictor for
dropout in IPMPs (5), the findings of our qualitative
study indicated that it is important to take educational
level into account 1 . This study on health literacy in
Oosterhaven J, Wittink H, Pell CD, Schröder CD, Popma H, Spierenburg
L, Devillé W. Health literacy and pain neuro education: a qualitative study
on patient perspectives. 2019. Manuscript submitted for publication.
1
www.medicaljournals.se/jrm
IPMPs emphasizes that to engage patients with low
health literacy levels (which is strongly associated
with low educational levels) a more tailored IPMP is
needed for patients to make sense of health informa-
tion in pain neuro-science education 1 . Further research
in other pain management programmes should reveal
whether participants with low educational levels (low
health literacy levels) are more prone to dropout.
Pain duration may be considered as an important
potential predictor for dropout based on the results
of the current study: we found a greater proportion of
participants with chronic pain for more than 5 years
in the DG than the CG. To date, pain duration has not
been investigated for an association with dropout in
other interdisciplinary pain management programmes.
However, our systematic review identified length of
disability and duration of work disability as predictors
for dropout (5). Pain duration is related to length of
disability; therefore this could be an interesting poten-
tial predictor for dropout for future research.
All dropouts scored worse on all items of the brief
IPQ and the TBQ. Just 2 items were eligible for inclu-
sion in the multivariate logistic regression analyses: the
Brief IPQ treatment control item and the item practical
barriers of the TBQ. Although in a recently published
meta-analysis (31) questions were raised with regard to
the predictive capacity of the E-CSM of Self-Regulation
in association with outcomes, our study indicates that it
may be important to consider patients’ views regarding
their treatment at baseline in association with dropout.
This is line with recommendations from 2 studies on
dropout in the mental health literature, which empha-
sized the importance of the identification of patients’
treatment expectations at the start of the treatment (32,
33). Further research should focus on confirmation and
external validation to confirm whether these beliefs are
important potential predictors for dropout.
Our finding that patients who had lower scores on
the PSEQ total score, were more likely to dropout from
this interdisciplinary pain management programme,
was similar to the findings from a retrospective cohort
study in an inpatient interdisciplinary pain programme
(4). A meta-analysis revealed self-efficacy as a key in-
fluence on chronic pain outcomes, and it is identified as
an important risk and protective factor for functioning
in patients with chronic pain (34). Thus, we suggest
that pain self-efficacy (as measured with the PSEQ)
be taken into account in practice in IPMPs. Additional
research is needed to investigate whether pain self-
efficacy is an important predictor for dropout in IPMPs.
With regard to anxiety and depression, our results
contrasted with findings from other research. Howard
et al. (35) found significant associations with dropout
for anxiety and depression, which we could not confirm